How to Refactor this Code? An Exploratory Study on Developer-ChatGPT Refactoring Conversations

TL;DR

This study analyzes nearly 18,000 developer-ChatGPT conversations to understand how developers communicate refactoring needs and how ChatGPT responds, revealing common patterns and prompting strategies in AI-assisted code improvement.

Contribution

It provides the first large-scale analysis of developer-ChatGPT refactoring conversations, highlighting communication patterns and implications for tool development and best practices.

Findings

Developers use both generic and specific refactoring terms.

ChatGPT responses often include explicit refactoring intentions.

Various prompting strategies influence ChatGPT's assistance in refactoring.

Abstract

Large Language Models (LLMs), like ChatGPT, have gained widespread popularity and usage in various software engineering tasks, including refactoring, testing, code review, and program comprehension. Despite recent studies delving into refactoring documentation in commit messages, issues, and code review, little is known about how developers articulate their refactoring needs when interacting with ChatGPT. In this paper, our goal is to explore conversations between developers and ChatGPT related to refactoring to better understand how developers identify areas for improvement in code and how ChatGPT addresses developers' needs. Our approach relies on text mining refactoring-related conversations from 17,913 ChatGPT prompts and responses, and investigating developers' explicit refactoring intention. Our results reveal that (1) developer-ChatGPT conversations commonly involve generic and specific terms/phrases; (2) developers often make generic refactoring requests, while ChatGPT typically includes the refactoring intention; and (3) various learning settings when prompting ChatGPT in the context of refactoring. We envision that our findings contribute to a broader understanding of the collaboration between developers and AI models, in the context of code refactoring, with implications for model improvement, tool development, and best practices in software engineering.